Nakata, T, Phillips, N, Simoes, P et al. (4 more authors) (2020) Aerodynamic imaging by mosquitoes inspires a surface detector for autonomous flying vehicles. Science, 368 (6491). pp. 634-637. ISSN 0036-8075
Abstract
Some flying animals use active sensing to perceive and avoid obstacles. Nocturnal mosquitoes exhibit a behavioral response to divert away from surfaces when vision is unavailable, indicating a short-range, mechanosensory collision-avoidance mechanism. We suggest that this behavior is mediated by perceiving modulations of their self-induced airflow patterns as they enter a ground or wall effect. We used computational fluid dynamics simulations of low-altitude and near-wall flights based on in vivo high-speed kinematic measurements to quantify changes in the self-generated pressure and velocity cues at the sensitive mechanosensory antennae. We validated the principle that encoding aerodynamic information can enable collision avoidance by developing a quadcopter with a sensory system inspired by the mosquito. Such low-power sensing systems have major potential for future use in safer rotorcraft control systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works http://www.sciencemag.org/about/science-licenses-journal-article-reuse. This is an article distributed under the terms of the Science Journals Default License.This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on 08 May 2020, volume 368, DOI: http://doi.org/10.1126/science.aaz9634 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Funding Information: | Funder Grant number Royal Society UF120079 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 May 2020 09:02 |
Last Modified: | 29 Jun 2020 16:04 |
Status: | Published |
Publisher: | American Association for the Advancement of Science |
Identification Number: | 10.1126/science.aaz9634 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159999 |